Modern manufacturing facilities often utilize a variety of robots to automate production processes. Robots may be arranged in cells, wherein several robots each perform the same process. For example, several robots may all be configured to perform an identical welding process on a work piece. Alternately, several robots may be utilized on an assembly line, wherein each robot performs unique steps of a production sequence.
Although robots are effective for maximizing efficiency, they are not without drawbacks. Unlike their human counterparts, robots are generally unable to communicate when they may experience a problem. For example, bearings or encoders of the robot may fail after a period of time without warning based on variable operating conditions, such as travel distances, temperatures, and load conditions.
Under standard operating conditions, maintenance periods may be scheduled at regular intervals. However, regularly scheduled intervals may be excessive when operating conditions are less extreme than standard, resulting in components being replaced prematurely, and unnecessarily increasing maintenance costs.
Alternatively, regularly scheduled intervals may be insufficient where operating conditions are more extreme than standard. In this instance, the robots may experience unexpected problems before the scheduled maintenance period. Unexpected failures are particularly problematic in the case of high-volume production facilities for a variety of reasons.
First, production facilities generally try to minimize the number of spare parts that are inventoried in-house in an effort to minimize costs. Accordingly, replacement parts must often be ordered. In the case of robots, many replacement parts may have long lead times, resulting in extended periods of time that the robot remains inoperable.
Additionally, production schedules are generally planned days or weeks in advance, wherein each of the robots in the production facility is expected to output a predetermined amount of work. Unexpected downtime of a single robot may negatively impact an entire production facility, as manufacturing processes downstream of the inoperable robot may be starved of expected work pieces. As a result, production may fall behind schedule.
Accordingly, there exists a need in the art for a system and method for proactively determining necessary maintenance and optimization of robots in order to schedule and minimize downtime, extend mechanical life of the robot, and reduce maintenance costs.